Distance to empty soft sensor for ford escape electric vehicle

Abstract

Electric vehicle (EV) drivers require reliable distance to empty (DTE) indication to plan their trips. In the current study, feed forward neural networks based soft sensors were designed to accurately predict DTE in a Ford Escape EV. The proposed DTE soft sensors were trained on actual drive cycle data and rated DTE using Levenberg Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms. Regression models were also developed for comparisons. Primary results show that the Bayesian Regularization trained soft sensor network with eleven hidden layer neurons achieved the highest testing accuracy (99.64%) among the two layered networks, followed by the Levenberg Marquardt (two layered, eleven hidden layer neurons, testing accuracy 99.62%) and Scaled Conjugate Gradient trained networks (two layered, seven hidden layer neurons, testing accuracy 99.49%). The linear and non linear regression models attained 96.19% and 97.53% accuracies respectively. Deeper soft sensor networks yielded better prediction accuracies at higher computation times. The five layered Bayesian Regularization trained network (with ten neurons in each hidden layer) maximized DTE prediction accuracy to 99.89%, but at the cost of 1175% more training time as compared to the best performing two layered network soft sensor. An optimal choice of prediction accuracy considering reasonable computation timescales can help reduce range anxiety of EV users significantly.